Abdullahi DaniyanYu GongSangarapillai Lambotharan2025-04-252016-06-232016-05-012019-03-27DOI: 10.1109/RADAR.2016.7485219http://repository.futminna.edu.ng:4000/handle/123456789/988We investigate a game theoretic data association technique for multi-target tracking (MTT) with varying number of targets. The problem of target state-estimate-to-track data association has been considered. We use the SMC-PHD filter to handle the MTT aspect and obtain target state estimates. We model the interaction between target tracks as a game by considering them as players and the set of target state estimates as strategies. Utility functions for the players are defined and a regret-based learning algorithm with a forgetting factor is used to find the equilibrium of the game. Simulation results are presented to demonstrate the performance of the proposed technique.Multi-target tracking (MTT)data associationgame theorycorrelated-equilibriumforgetting factorregret matchingparticle filtersequential Monte Carlo (SMC)probability hypothesis density (PHD) filterOther